





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_A					:				Union[str, Any]				=			logging.get_logger(__name__)



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_   ,				**A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			feature_size
        SCREAMING_SNAKE_CASE__											=			sampling_rate
        SCREAMING_SNAKE_CASE__											=			padding_value

        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''padding_side'''   ,				'''right'''     )
        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''return_attention_mask'''   ,				A_     )

        super().__init__(**A_     )



    def  lowercase_					(       self   ,				A_   ,				A_ = True   ,				A_ = None   ,				A_ = False   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				):





        '''simple docstring'''

        if isinstance(A_   ,				(list, tuple)     ) and isinstance(processed_features[0]   ,				(dict, BatchFeature)     ):
            SCREAMING_SNAKE_CASE__											=			{
                key: [example[key] for example in processed_features] for key in processed_features[0].keys()
            }

        # The model's main input name, usually `input_values`, has be passed for padding
        if self.model_input_names[0] not in processed_features:
            raise ValueError(
                '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
                f''' to this method that includes {self.model_input_names[0]}, but you provided'''
                f''' {list(processed_features.keys()     )}'''     )

        SCREAMING_SNAKE_CASE__											=			processed_features[self.model_input_names[0]]
        SCREAMING_SNAKE_CASE__											=			(
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(A_     ) == 0:
            if return_attention_mask:
                SCREAMING_SNAKE_CASE__											=			[]
            return processed_features

        # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        SCREAMING_SNAKE_CASE__											=			required_input[0]
        if isinstance(A_   ,				(list, tuple)     ):
            # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
            SCREAMING_SNAKE_CASE__											=			0
            while len(required_input[index]     ) == 0:
                index += 1
            if index < len(A_     ):
                SCREAMING_SNAKE_CASE__											=			required_input[index][0]

        if return_tensors is None:
            if is_tf_tensor(A_     ):
                SCREAMING_SNAKE_CASE__											=			'''tf'''
            elif is_torch_tensor(A_     ):
                SCREAMING_SNAKE_CASE__											=			'''pt'''
            elif isinstance(A_   ,				(int, float, list, tuple, np.ndarray)     ):
                SCREAMING_SNAKE_CASE__											=			'''np'''
            else:
                raise ValueError(
                    f'''type of {first_element} unknown: {type(A_     )}. '''
                    '''Should be one of a python, numpy, pytorch or tensorflow object.'''     )

        for key, value in processed_features.items():
            if isinstance(value[0]   ,				(int, float)     ):
                SCREAMING_SNAKE_CASE__											=			to_numpy(A_     )
            else:
                SCREAMING_SNAKE_CASE__											=			[to_numpy(A_     ) for v in value]

        # Convert padding_strategy in PaddingStrategy
        SCREAMING_SNAKE_CASE__											=			self._get_padding_strategies(padding=A_   ,				max_length=A_     )

        SCREAMING_SNAKE_CASE__											=			processed_features[self.model_input_names[0]]

        SCREAMING_SNAKE_CASE__											=			len(A_     )
        if not all(len(A_     ) == batch_size for v in processed_features.values()     ):
            raise ValueError('''Some items in the output dictionary have a different batch size than others.'''     )

        SCREAMING_SNAKE_CASE__											=			[]
        for i in range(A_     ):
            SCREAMING_SNAKE_CASE__											=			{k: v[i] for k, v in processed_features.items()}
            # truncation
            SCREAMING_SNAKE_CASE__											=			self._truncate(
                A_   ,				max_length=A_   ,				pad_to_multiple_of=A_   ,				truncation=A_   ,				)
            truncated_inputs.append(A_     )

        if padding_strategy == PaddingStrategy.LONGEST:
            # make sure that `max_length` cannot be longer than the longest truncated length
            SCREAMING_SNAKE_CASE__											=			max(len(input_slice[self.model_input_names[0]]     ) for input_slice in truncated_inputs     )
            SCREAMING_SNAKE_CASE__											=			PaddingStrategy.MAX_LENGTH

        SCREAMING_SNAKE_CASE__											=			{}
        for i in range(A_     ):
            # padding
            SCREAMING_SNAKE_CASE__											=			self._pad(
                truncated_inputs[i]   ,				max_length=A_   ,				padding_strategy=A_   ,				pad_to_multiple_of=A_   ,				return_attention_mask=A_   ,				)

            for key, value in outputs.items():
                if key not in batch_outputs:
                    SCREAMING_SNAKE_CASE__											=			[]
                if value.dtype is np.dtype(np.floataa     ):
                    SCREAMING_SNAKE_CASE__											=			value.astype(np.floataa     )
                batch_outputs[key].append(A_     )

        return BatchFeature(A_   ,				tensor_type=A_     )



    def  lowercase_					(       self   ,				A_   ,				A_ = None   ,				A_ = PaddingStrategy.DO_NOT_PAD   ,				A_ = None   ,				A_ = None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
            SCREAMING_SNAKE_CASE__											=			len(A_     )

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
            SCREAMING_SNAKE_CASE__											=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        SCREAMING_SNAKE_CASE__											=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A_     ) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
            SCREAMING_SNAKE_CASE__											=			np.ones(len(A_     )   ,				dtype=np.intaa     )

        if needs_to_be_padded:
            SCREAMING_SNAKE_CASE__											=			max_length - len(A_     )
            if self.padding_side == "right":
                if return_attention_mask:
                    SCREAMING_SNAKE_CASE__											=			np.pad(
                        processed_features['''attention_mask''']   ,				(0, difference)     )
                SCREAMING_SNAKE_CASE__											=			((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                SCREAMING_SNAKE_CASE__											=			np.pad(
                    A_   ,				A_   ,				'''constant'''   ,				constant_values=self.padding_value     )
            elif self.padding_side == "left":
                if return_attention_mask:
                    SCREAMING_SNAKE_CASE__											=			np.pad(
                        processed_features['''attention_mask''']   ,				(difference, 0)     )
                SCREAMING_SNAKE_CASE__											=			((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                SCREAMING_SNAKE_CASE__											=			np.pad(
                    A_   ,				A_   ,				'''constant'''   ,				constant_values=self.padding_value     )
            else:
                raise ValueError('''Invalid padding strategy:''' + str(self.padding_side     )     )

        return processed_features



    def  lowercase_					(       self   ,				A_   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				):





        '''simple docstring'''

        if not truncation:
            return processed_features
        elif truncation and max_length is None:
            raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''     )

        SCREAMING_SNAKE_CASE__											=			processed_features[self.model_input_names[0]]

        # find `max_length` that fits `pad_to_multiple_of`
        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
            SCREAMING_SNAKE_CASE__											=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        SCREAMING_SNAKE_CASE__											=			len(A_     ) > max_length

        if needs_to_be_truncated:
            SCREAMING_SNAKE_CASE__											=			processed_features[self.model_input_names[0]][:max_length]
            if "attention_mask" in processed_features:
                SCREAMING_SNAKE_CASE__											=			processed_features['''attention_mask'''][:max_length]

        return processed_features




    def  lowercase_					(       self   ,				A_=False   ,				A_=None     ):





        '''simple docstring'''

        if padding is not False:
            if padding is True:
                SCREAMING_SNAKE_CASE__											=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
            elif not isinstance(A_   ,				A_     ):
                SCREAMING_SNAKE_CASE__											=			PaddingStrategy(A_     )
            elif isinstance(A_   ,				A_     ):
                SCREAMING_SNAKE_CASE__											=			padding
        else:
            SCREAMING_SNAKE_CASE__											=			PaddingStrategy.DO_NOT_PAD

        # Set max length if needed
        if max_length is None:
            if padding_strategy == PaddingStrategy.MAX_LENGTH:
                raise ValueError(
                    f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''     )

        # Test if we have a padding value
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
            raise ValueError(
                '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
                ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''     )

        return padding_strategy




def 							UpperCamelCase_(					_A :list[list]	)->		list[list]:
			UpperCamelCase__       =							current_set.copy()
			for row_index, row in enumerate(_A	):
						UpperCamelCase__       =							row[0]
						for column_index, column in enumerate(_A	):
									if magnitude == 0:
												UpperCamelCase__       =							column
												continue
									UpperCamelCase__       =							column / magnitude
    # Subtract to cancel term
			UpperCamelCase__       =							current_set[0]
			UpperCamelCase__       =							[first_row]
			UpperCamelCase__       =							current_set[1::]
			for row in current_set:
						UpperCamelCase__       =							[]
						# If first term is 0, it is already in form we want, so we preserve it
						if row[0] == 0:
									final_set.append(_A	)
									continue
						for column_index in range(len(_A	)	):
									temp_row.append(first_row[column_index] - row[column_index]	)
						final_set.append(_A	)
			# Create next recursion iteration set
			if len(final_set[0]	) != 3:
						UpperCamelCase__       =							final_set[0]
						UpperCamelCase__       =							[]
						UpperCamelCase__       =							[]
						for row in final_set[1::]:
									current_first_column.append(row[0]	)
									next_iteration.append(row[1::]	)
						UpperCamelCase__       =							simplify(_A	)
						for i in range(len(_A	)	):
									resultant[i].insert(0					,  current_first_column[i]	)
						resultant.insert(0					,  _A	)
						UpperCamelCase__       =							resultant
			return final_set
def 							UpperCamelCase_(					_A :list[list]	)->		list:
			if len(_A	) == 0:
						raise IndexError("solve_simultaneous() requires n lists of length n+1"	)
			UpperCamelCase__       =							len(_A	) + 1
			if any(len(_A	) != _length for item in equations	):
						raise IndexError("solve_simultaneous() requires n lists of length n+1"	)
			for row in equations:
						if any(not isinstance(_A					,  (int, float)	) for column in row	):
									raise ValueError("solve_simultaneous() requires lists of integers"	)
			if len(_A	) == 1:
						return [equations[0][-1] / equations[0][0]]
			UpperCamelCase__       =							equations.copy()
			if any(0 in row for row in data_set	):
						UpperCamelCase__       =							data_set.copy()
						UpperCamelCase__       =							[]
						for row_index, row in enumerate(_A	):
									if 0 not in row:
												UpperCamelCase__       =							data_set.pop(_A	)
												break
						if not full_row:
									raise ValueError("solve_simultaneous() requires at least 1 full equation"	)
						data_set.insert(0					,  _A	)
			UpperCamelCase__       =							data_set.copy()
			UpperCamelCase__       =							simplify(_A	)
			UpperCamelCase__       =							simplified[::-1]
			UpperCamelCase__       =							[]
			for row in simplified:
						UpperCamelCase__       =							row[-1]
						if not solutions:
									if row[-2] == 0:
												solutions.append(0	)
												continue
									solutions.append(current_solution / row[-2]	)
									continue
						UpperCamelCase__       =							row.copy()[: len(_A	) - 1 :]
						while temp_row[0] == 0:
									temp_row.pop(0	)
						if len(_A	) == 0:
									solutions.append(0	)
									continue
						UpperCamelCase__       =							temp_row[1::]
						UpperCamelCase__       =							temp_row[::-1]
						for column_index, column in enumerate(_A	):
									current_solution -= column * solutions[column_index]
						solutions.append(_A	)
			UpperCamelCase__       =							[]
			for item in solutions:
						final.append(float(round(_A					,  5	)	)	)
			return final[::-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__UpperCamelCase     		=							[
						    [2, 1, 1, 1, 1, 4],
						    [1, 2, 1, 1, 1, 5],
						    [1, 1, 2, 1, 1, 6],
						    [1, 1, 1, 2, 1, 7],
						    [1, 1, 1, 1, 2, 8],
						]
						print(solve_simultaneous(eq))
						print(solve_simultaneous([[4, 2]]))


